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Adaptation of the random forest method
Proceedings of the 4th International Conference on Smart City Applications, 2019Random Forest Algorithm is a method of machine learning that refers to train individual classifiers and aggregates their predictors. It is specifically reserved to decision tree classifiers and used for classification and regression problems in several areas.
Mourad Azhari +4 more
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Infrared bound states in the continuum: random forest method
Optics Letters, 2023In this Letter, we consider optical bound states in the continuum (BICs) in the infrared range supported by an all-dielectric metasurface in the form of subwavelength dielectric grating. We apply the random forest machine learning method to predict the frequency of the BICs as dependent on the optical and geometric parameters of the metasurface.
M. S. Molokeev +5 more
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A random forest method for obsolescence forecasting
2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2017Driven by the frequent technological changes and innovation, obsolescence has become a major challenge that cannot be ignored in which the life cycle of the components is often shorter than that of their systems. Basically, obsolescence problems are often sudden and not planned which causes delays and extra costs.
Y. Grichi, Y. Beauregard, T. M. Dao
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Mindful in a random forest: Assessing the validity of mindfulness items using random forests methods
Personality and Individual Differences, 2015Abstract Whereas the number of studies supporting the efficacy of mindfulness as a health intervention is increasing, the measurement of mindfulness remains a subject of debate. Given the importance of measurement in this field, this paper aims to further our understanding of the assessment of mindfulness by employing an approach referred to as ...
Sebastian Sauer +4 more
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Seismic Interpolation Based on the Random Forest Method
82nd EAGE Annual Conference & Exhibition, 2021Summary In course of any seismic data acquisition, one inevitably encounters instances of empty seismic traces or insufficient spatial sampling, which results in bad sectors and can greatly affect seismic data quality. It is therefore often necessary to undertake seismic trace interpolation to solve this problem. In this paper, a machine learning based
K. Xu, S. Chen, J. Liu, D. Jiang, Y. Qu
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A Random Forest-based ensemble method for activity recognition
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015This paper presents a multi-sensor ensemble approach to human physical activity (PA) recognition, using random forest. We designed an ensemble learning algorithm, which integrates several independent Random Forest classifiers based on different sensor feature sets to build a more stable, more accurate and faster classifier for human activity ...
Zengtao Feng, Lingfei Mo, Meng Li
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Classification of Erythematosquamous Dermatosis by the Method of Random Forest
Journal of Dermatology Research Reviews & Reports, 2023Machine Learning (ML) methods have found wide applications in dermatology (Chan et al., 2020) [1]. Thomsen, Iversen, Titlestad & Winther (2020) reviewed 2175 publications and found that the most common usage of ML methods was in the binary classification of malignant melanoma from images [2].
Ashok K Singh, Dwaiypayan Mukhopadhyay
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Promotion Recommendation Method and System Based on Random Forest
Proceedings of the 5th Multidisciplinary International Social Networks Conference, 2018Living in a sharply competitive telecom market, customers are provided with a great variety of promotions of telecom offers which are excessive and complex in recent years. Not only customers have no idea how to choose the suitable promotions but also front-line sales cannot recommend suitable promotions depending on merely the traditional ...
Wan-Hsun Hu +4 more
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The random subspace method for constructing decision forests
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom resolved. A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as ...
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A New Method for Text Verification Based on Random Forests
2012 International Conference on Frontiers in Handwriting Recognition, 2012Text in image or video frames contains a lot of high-level semantics which can be useful for multimedia indexing, management. Coarse text detection results may contain many false alarms, which makes it necessary to eliminate the false alarms for further recognition.
Yang Zhang +3 more
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